Feature discovery in relevance feedback using pattern mining

Luepol Pipanmaekaporn
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引用次数: 7

Abstract

It is a big challenge to guarantee the quality of extracted features in text documents to describe user interests or preferences due to large amounts of noise. Over the years, pattern mining-based approaches to RF have attracted great interest to discover knowledge of user interest from text documents. However, the data mining approaches often produce a large set of patterns, which include a lot of noisy patterns, reducing the effective use of pattern mining. In this paper, we present a novel pattern mining approach to RF. This approach mines patterns in both positive and negative feedback and then classifies them into clusters to find user-specific patterns. We also propose a novel pattern deploying method that effectively uses the discovered patterns for improving the performance of searching relevant documents. Experiments are conducted on Reuters Corpus Volume 1 data collection (RCV1) and TREC filtering topics. The results show that the proposed approach achieves promising performance comparing with state-of-the-art term-based methods and pattern-based ones.
基于模式挖掘的关联反馈特征发现
由于大量的噪声,如何保证文本文档中描述用户兴趣或偏好的提取特征的质量是一个很大的挑战。多年来,基于模式挖掘的RF方法吸引了人们对从文本文档中发现用户感兴趣的知识的极大兴趣。然而,数据挖掘方法通常会产生大量的模式集,其中包含大量的噪声模式,从而降低了模式挖掘的有效利用。本文提出了一种新的射频模式挖掘方法。这种方法挖掘正反馈和负反馈中的模式,然后将它们分类到集群中,以找到特定于用户的模式。我们还提出了一种新的模式部署方法,有效地利用发现的模式来提高搜索相关文档的性能。在路透社语料库卷1数据收集(RCV1)和TREC过滤主题上进行了实验。结果表明,与目前最先进的基于术语的方法和基于模式的方法相比,该方法取得了令人满意的性能。
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